Allergies currently affect approximately 34% of the general population (Linneberg 2000). Whilst at one extreme serious conditions such as anaphylaxis can be life threatening, most allergic disorders pose little risk of death. However, diseases such as urticaria and angioedema cause distress and misery for millions of patents, often at times in their lives when they should be most active (Holgate and Broide 2003). Urticaria (or hives) is a skin rash which occurs in the upper dermis. Angioedema (also known as Quincke's edema) is a swelling of the dermis, subcutaneous tissue, mucosa and submucosal tissues. Allergic diseases are a significant cause of morbidity in modern society, adversely affecting sleep, intellectual functioning and recreational activities. Furthermore, allergic diseases exert a profoundly negative impact on occupational performance and have major public health costs.
Across the United Kingdom, waiting times for specialist allergy consultations following referral from primary care are long.
The rising prevalence of allergies and the associated demand for specialist services suggest that waiting times will inevitably lengthen over the course of the next decade. Given that there is currently an acute shortage of Immunologists and Allergists in the UK and worldwide, it seems unlikely that sufficient medical manpower will emerge in the foreseeable future to deal with this increasing demand.
Recent in-house research has centred on the role of the Allergy Nurse Practitioner in the diagnosis and management of allergic disease. Increasing use of the Nurse Practitioner in a diagnostic role would enable waiting times to be shortened and new patient referrals to be seen without the presence of the Consultant Clinical Immunologist. Whilst Nurse Practitioner-based diagnosis and management strategies should, in time, ameliorate the critical situation, a parallel increase in demand for allergy services will, without doubt, limit the positive effects on waiting times. There therefore remains a need to develop further innovative methods to facilitate access of patients to clinical diagnostic services.
However, as one would expect, it is extremely important that any new methods of diagnosis are accurate if they are to be adopted by the medical community at large. These methods must be able to replicate, it not exceed, the accuracy of an experienced Clinical Immunologist. This is a difficult task to achieve because a Clinical Immunologist uses information from a vast number of sources when reaching a diagnosis.
Typically, when diagnosing a condition, a medical practitioner will integrate information from several sources, such as a medical history, a physical examination, the results of clinical tests, and by asking the patient about his/her condition. The medical practitioner will use judgement based on experience and intuition, both when deciding what to look for and in analysing the information, in order to come to a particular diagnosis.
Thus, the process of diagnosis involves a combination of knowledge, intuition and experience that leads a medical practitioner to ask certain questions and carry out particular clinical tests, and the validity of the diagnosis is very dependent upon these factors.
Given the predictive and intuitive nature of medical diagnosis, and the fact that specialist, experienced medical practitioners are in demand, we have attempted to replicate the diagnostic process in an automated system, in order to give a wider audience access to this service. We have found that artificial neural networks (ANNs) have characteristics that make them particularly well suited for this purpose.
ANNs are computational mathematical modelling tools for information processing and may be defined as ‘structures comprised of densely interconnected adaptive processing elements (nodes) that are capable of performing massively parallel computations for data processing and knowledge representation’ (Hecht-Nielsen 1990; Schalkoff 1977). Single artificial neurons for the computation of arithmetic and logical functions were first described by McCulloh and Pitts (1943); fifteen years later Rosenblatt (1958) described the first successful neurocomputer (the Mark 1 Perceptron). This simple network consisted of two layers of neurons connected by a single layer of weighted links and was capable of solving problems in a way analogous to information processing in the human brain (Wei et al 1998; Basheer and Hajmeer 2000). These early structures were however unable to predict generalised solutions for complex non-linear problems. Over the course of the following five decades complexity has increased with the development of multiple networked perceptrons; such advances have led to the application of ANNs to a colossal number of problems, and by 1994 more than 50 different types of network were in existence (Pham 1994 and Basheer and Hajmeer 2000), each possessing unique properties enabling them to solve particular tasks.
Such ANNs are capable of dealing with non-linear data, fault and failure, high parallelism and imprecise and fuzzy information (Wei et al 1998). Neural networks have been shown to be capable of modelling complex real-world problems and found extensive acceptance in many scientific disciplines (Callan 1999). The decision as to which type of ANN should be utilised for a particular task depends on problem logistics, input type, and the execution speed of the trained network (Basheer and Hajmeer 2000).
Neural networks have found increasing application in a range of clinical settings where they have produced accurate and generalised solutions compared to traditional statistical methodology (reviewed Baxt 1995, Wei et al 1998, Dybowski and Gant 2001). For example, U.S. Pat. No. 6,678,669 discloses using an ANN to diagnose endometriosis, predicting pregnancy related events, such as the likelihood of delivery within a particular time period, and other such disorders relevant to women's health.
The most commonly used ANN in such studies is the Backpropagational Multilayer Perceptron (MLP). MLPs are particularly useful in solving pattern classification problems (Wei et al 1998; Basheer and Hajmeer, 2000), which are common in the clinical arena. In this context the ANN looks for patterns in a similar way to learning in the human mind; the more a particular pattern is represented, the stronger the recognition of it by the network.
We have developed a method of diagnosing urticaria and angioedema using a neural network. In particular, from the vast amount of information that a clinician would have available, we have identified a manageable set of questions and tests that have clinical significance, and can be used to train a neural network to diagnose urticaria and angioedema, and by inputting the results of these questions and tests into a neural network thus trained the network to produce a diagnosis.
Surprisingly, we have found that a small set of just 5 inputs to the neural network have a profound influence on the provision of an accurate diagnosis. The 5 inputs are generated by asking a patient 4 questions and carrying out 1 medical test, and are referred to herein as the 5-input model.
We have also identified a set of 14 (13 questions and 1 test), 15 (13 questions and 2 tests) 17 (14 questions and 3 tests), 21 (16 questions and 5 tests), 25 (20 questions and 5 tests), 35 (23 questions and 12 tests), 54 (36 questions and 17 tests), and 79 (42 questions and 36 tests) inputs, referred to herein as the 14, 15, 17, 21, 25, 35, 54 and 79 input models respectively, that can be input into a neural network to obtain a diagnosis.
The identification of these clinically significant questions and tests will mean that a neural network can be trained to diagnose urticaria or angioedema in considerably less time than it currently takes a consultant, which in turn will save time and money.
Additionally, a neural network offers an easy-to-use means of diagnosis, both for clinicians and non-clinicians, and will allow central aspects of diagnosis and management to be performed electronically in a way that is accessible to systematic audit and reduce inequalities in accessing allergy services, via the use of remote electronic information transfer.
For the avoidance of doubt, any reference herein to a neural network is a reference to an artificial neural network (ANN).